See how hallucination monitoring works on your AI workflow.
Hallucination Monitor
Flag AI-generated content that is internally weak, under-grounded, or suspiciously overconfident. Route risky claims for review when they appear, with a clearer read on why they look shaky.
The problem
Confident output, weak grounding.
AI systems produce polished, confident text even when their internal state suggests weak grounding or likely hallucination. The quality of the prose hides the risk of the content. In high-stakes domains, this is expensive.
What Concordance does
Score claims, not just outputs.
Concordance decomposes generated content into claims and scores each one for internal confidence mismatch. Claims that look strong on the surface but are internally weak get flagged for review, fallback, or escalation.
How It Works
01
Decompose claims
Break generated output into individual claims and assertions that can be independently assessed for internal grounding.
02
Score internal confidence
Each claim is scored using activation-informed signals that indicate whether the model's internal state supports the confidence of its output.
03
Route for review
Claims that score as internally weak, under-grounded, or overconfident are flagged for human review, fallback handling, or escalation.
Who Benefits
Financial Research & Analysis
Flag weak grounding in investment research, underwriting reports, and risk assessments when it shows up in the work.
Legal Workflows
Detect hallucinated citations, unsupported claims, and overconfident analysis in AI-assisted document review.
Enterprise Knowledge Systems
Monitor search and synthesis tools for confidently stated but internally weak answers to employee queries.
Healthcare-Adjacent AI
Flag potentially dangerous medical or wellness claims that sound authoritative but lack internal grounding.
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